Computer-implemented systems and methods for mood state determination

ABSTRACT

Computer-implemented systems and methods are provided for determining an overall mood score of a document. For example, the document is received from a computer-readable medium. A text segment in a document is identified to be indicative of a mood of the document. The text segment is mapped to a mood scale among a predetermined set of mood scales. A mood weight associated with the mood scale for the text segment is generated. An overall mood score of the document is determined based at least in part on the mood weight.

This application is a continuation patent application of U.S. patentapplication Ser. No. 13/483,504, filed on May 30, 2012 and entitled“Computer-Implemented Systems and Methods for Mood State Determination”.The entirety of this priority application is incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates generally to the field of data analysisand, more specifically, to computer-implemented systems and methods formood state determination of documents.

BACKGROUND

Moods or sentiments often are expressed in text documents distributedthrough the internet or other communication media, such as blog entries,tweets, posts on social. networking websites, online conversations, andnewspaper articles. Analysis of the moods or sentiments expressed bysuch text documents has become a valuable tool for various purposes,including marketing, customer relationship management, politicalanalysis, and brand analysis.

SUMMARY

As disclosed herein, computer-implemented systems and methods areprovided for determining an overall mood score of a document. Forexample, the document is received from a computer-readable medium. Atext segment in a document is identified to be indicative of a mood ofthe document. The text segment is mapped to a mood scale among apredetermined set of mood scales. A mood weight associated with the moodscale for the text segment is generated. An overall mood score of thedocument is determined based at least in part on the mood weight.

As another example, a system for determining an overall mood score of adocument includes one or more data processors and a computer-readablestorage medium. The computer-readable storage medium is encoded withinstructions for commanding the one or more data processors to executesteps including, identifying a text segment in a document that isindicative of a mood of the document, mapping the text segment to a moodscale among a predetermined set of mood scales, generating a mood weightassociated with the mood scale for the text segment, and determining anoverall mood score of the document based on the mood weight.

As another example, a non-transitory computer-readable storage mediumcomprises programming instructions for determining an overall mood scoreof a document. The programming instructions are adapted to cause aprocessing system to execute steps including, identifying a text segmentin a document that is indicative of a mood of the document, mapping thetext segment to a mood scale among a predetermined set of mood scales,generating a mood weight associated with the mood scale for the textsegment, and determining an overall mood score of the document based onthe mood weight.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a configuration of software components formood. state measurements.

FIG. 2 depicts an example of the mood scales as shown in FIG. 1.

FIG. 3 depicts an example of calculating mood scores for different moodsof a text document.

FIG. 4 depicts an example of a configuration of software components formood state measurements in various contexts.

FIG. 5 depicts an example flow diagram for determining an overall moodscore of a document.

FIG. 6 depicts a computer-implemented environment wherein users caninteract with a mood state measurement system hosted on one or moreservers through one or more networks.

FIG. 7 depicts a mood state measurement system provided on a stand-alonecomputer for access by a user.

DETAILED DESCRIPTION

Sentiment analysis of a text document often focuses on determiningwhether the text document indicates a positive sentiment, a negativesentiment or a neutral sentiment. However, such sentiment analysis lacksdepth and analytic variables. A text document may be determined toindicate a negative sentiment, but whether the document indicates a verynegative sentiment or a little negative sentiment or whether thenegative sentiment results from depression, fear or anger cannot bedetermined from the sentiment analysis. In addition, sentiment analysisoften requires complex linguistic rules which are usually developed in atime-consuming process on a project-specific basis and/or on anindustry-specific basis. Even with these complex linguistic rules, highaccuracy of the sentiment analysis is often difficult to achieve.Instead, mood state measurements which provide more analyticalvariables, better depth and reusability over differentprojects/industries can be used for document analysis.

FIG. 1 depicts at 100 an example of a configuration of softwarecomponents for mood state measurements. Text segments 104 indicatingmoods are identified in one or more documents 102 for mood statemeasurements 106 to generate an overall mood score 108 for the one ormore documents 102. A set of mood scales 110 are implemented for themood state measurements 106.

Particularly, a text document 102, i.e., a set of written words of anylength, can include one or more text segments 104 that each indicate amood with a varying degree (e.g., a little happy, happy, or very happy).Each of the text segments 104, e.g., a word, a phrase, a sentence, or aparagraph, can be mapped to one of the mood scales 110 and be assigned amood weight associated with the particular mood scale. An overall moodscore can be determined for the document 102 based on the mood scales110 and the mood weights of the text segments 104.

FIG. 2 depicts an example of the mood scales 110. As shown in FIG. 2,the mood scales 110 include six positive moods: “composed,” “confident,”“clearheaded,” “energetic,” “agreeable” and “elated,” and six negativemoods: “anxious,” “unsure,” “confused,” “tired,” “hostile” and“depressed.” A positive mood and a corresponding negative moodconstitute a mood scale.

Referring to FIG. 1 and FIG. 2, if the mood indicated by a text segment104 matches with a positive mood or a negative mood of a mood scale,such text segment can be mapped to the mood scale. If the text segmentindicates the positive mood of the mood scale, a positive weight can beassigned to the text segment. If the text segment indicates the negativemood of the mood scale, a negative weight can be assigned to the textsegment.

Such a text segment becomes a weighted classifier for the mood. As anexample, Table 1 shows some weighted classifiers for different moods.

TABLE 1 Weight Weight Weight Weight Composed (+) Anxious (−) Clearheaded(+) Confused (−) alright 1 annoyed 2 alert 3 absent 3 appease 2 agitated2 aware 2 absent 1 minded appeased 2 annoying 2 careful 2 ambiguous 2appeasing 2 anxiety 3 cautious 2 baffle 1 at ease 2 anxious 3clarification 2 baffled 1

If a text document includes a plurality of weighted classifiers of aparticular mood, the weights of these classifiers may be aggregated togenerate a mood score for the mood. If the text document includesclassifiers for both the positive mood and the negative mood in aparticular mood scale, the negative weights of the classifiers for thenegative mood can be subtracted from the positive weights of theclassifiers for the positive mood to generate a mood score for the moodscale.

In addition, linguistic modifiers, such as negations, amplifiers anddampers, associated with one or more classifiers in a text document canbe included in the mood state measurements of the text document. Forexample, the classifier “happy” indicates the positive mood “elated”with a mood weight of 2. If a negation “never” is associated with theclassifier “happy” (e.g., “never happy”), not only does the mood for theclassifier change from “elated” to “depressed” but also the mood weightof the classifier changes from 2 to 1. If an amplifier, such as “very,”is associated with the classifier “happy” (e.g., “very happy”), the moodweight of the classifier “happy” is increased from 2 to 3. If a damper,such as “sort of,” is associated with the classifier “happy” (e.g.,“sort of happy”), the mood weight of the classifier “happy” is decreasedfrom 2 to 1.

FIG. 3 depicts an example of calculating mood scores for different moodsof a text document. As shown in FIG. 3, text segments indicative of asnood are identified in the text document, including “downcast,”“disagreeable,” “hurt,” and “sobbing.” These text segments are mapped tothe mood scales as shown in FIG. 2, for example. Table 2 shows examplesof the moods indicated by the text segments and the corresponding moodweights. “Downcast,” “hurt,” and “sobbing” are mapped to the mood“depressed,” and “disagreeable” is mapped to the mood “hostile.” Themood weights of the classifiers are added up to generate mood scores forthe moods “depressed” and “disagreeable” respectively.

TABLE 2 Depressed Weight (−) Hostile Weight (−) Downcast 1 Disagreeable2 Hurt 1 Sobbing 3 Mood 5 2 Score(s)

The mood state measurements can be applied for document analysis invarious contexts, such as predictive analysis of information databasesand social media analysis. FIG. 4 depicts an example of a configurationof software components for mood state measurements in various contexts.As shown in FIG. 4, documents 406 are extracted from social media 108 orinformation databases 410 for mood state measurements 404. Mood statedatasets 402, such as mood scores for the documents 406, are generatedbased on the mood state measurements 404.

Specifically, the documents 406 may be extracted from the informationdatabases 410, e.g., financial information databases, retail informationdatabases, and political information databases, or from social media,e.g., social networking websites or bulletin boards. Specific filtersmay be implemented in order to obtain relevant documents related to aparticular company, a particular product, or a particular person.

A reference database may be pre-built for the mood state measurements404. For example, the reference database may include a plurality ofreference text segments indicative of different moods. In anotherexample, the reference database may include three datasets,“start-list,” “multiple-term,” and “mood-synonym.” The dataset“start-list” may contain all possible combinations of categories that areference text segment indicative of a mood could fall into. Thesecombinations are coded so that the mood types, scores and negation,amplifier and damper classifications for each reference text segment canbe extracted. As an example, a reference text segment (e.g., a word)that is linked to a code 2_AMX_zzz_AMP_(—)1_zzz_CONFIDENT_(—)3 would beassociated with two classifications: an amplifier and a mood. That is,the reference text segment can be mapped to the mood “confident” with ascore of 3, and can be classified as an amplifier with a score of 1. Thedataset “multiple-term” may contain a list of multi-term referencephrases indicative of different moods. Various syntaxes can be used inthe dataset “multiple-term”, such as “attracted to:3:Blank.” Inaddition, the dataset “mood-synonym” may include all the reference textsegments and the codes from the dataset “start-list” to which eachreference text segment is mapped. For instance, for the reference phrase“a little,” the matching code is “1_DXX_zzz_DAMPER_(—)1.”

The mood state measurements 404 may be performed for the documents 406based on the prebuilt reference database. For example, the documents 406are parsed to extract each text segment (e.g., each word or phrase) intoa new dataset “keys.” The dataset “keys” may include the numbers oftimes that each text segment occurs in the documents 406, the number ofdocuments that each text segment occurs in, whether or not the textsegment will be kept for mood scoring, a unique ID for each textsegment, and an ID of a parent text segment if there is one,

In addition, two datasets, e.g., “offset” and “out,” may also begenerated. The dataset “offset” may contain the location of each textsegment in each document and the dataset “out” may contain informationconcerning what text segments appear in each document and how manytimes. For example, the dataset “offset” may be ranked based on textsegment appearances per document, and then be reordered as if each ofthe documents 406 is parsed from left to right. The dataset “keys” andthe ranked “offset” dataset are then used to create a new datasetcontaining all text segments indicative of moods that are found. in thedocuments 406, the synonyms or codes for such text segments, the numberof times each text segment occurs per document, and the IDs of the textsegments.

Next, each text segment may be classified into one or more of aplurality of categories, e.g., mood, negation, amplifier or damper,according to the code for the text segment. A dataset may be created foreach of these categories. For example, the dataset of the mood categorymay contain a number of text segments of the mood category, the documentnumber in which each text segment appears in, the position of the textsegment in the document, and the number of times the text segmentappears per document.

The dataset of the mood category may be joined to each of the datasetsfor negation, amplifier and damper to generate three new datasets inorder to find the text segments of the mood category that appear in thedocuments 406 in close proximity to a text segment of one of theamplifier category, the damper category, and the negation category. Themood scores for the text segments of the mood category may be adjustedaccordingly. As an example, if a text segment of the amplifier categoryis less than 2 positions before a text segment of the mood category in adocument, then an amplifier value associated with the text segment ofthe mood category can be set to 1. If another text segment of theamplifier category is less than 2 positions before the text segment ofthe mood category in another document, then the amplifier valueassociated with the text segment of the mood category can be increased(e.g., to 2). In another example, if a text segment of the dampercategory is less than 2 positions before a text segment of the moodcategory in a document, then a damper value associated with the textsegment of the mood category can be set to −1. If another text segmentof the damper category is less than 2 positions before the text segmentof the mood category in another document, then the damper valueassociated with the text segment of the mood category can be increasedin magnitude (e.g., to −2). In yet another example, if a text segment ofthe negation category is less than 3 positions before a text segment ofthe mood category in a document, then a negation value associated withthe text segment of the mood category can be set to 1. If another textsegment of the negation category is less than 3 positions before thetext segment of the mood category in another document, then the negationvalue associated with the text segment of the mood category can beincreased.

Some text segments in the documents 406 may be classified as indicatingmore than one mood. For example, the word “content” may be classified asboth “composed” and “elated.” The code for “content” is2_MMX_zzz_COMPOSED_(—)2_zzz_ELATED_(—)1. A dataset may be created thatcontains a number of text segments, one or more moods each text segmentcan be classified as, and the mood scores corresponding to the one ormore moods the text segment can be classified as Table 3 shows anexample of such a dataset.

TABLE 3 TERM MOOD MOOD SCORE content composed 2 content elated 1 capableconfident 2 wilder energetic 3

The dataset can be joined to the datasets of the amplifier category, thedamper category, and the negation category in order to create a newdataset. An adjusted mood score for each text segment can be calculatedand included in the new dataset. For example, if the negation value ofthe text segment is not 1 or larger, and if a sum of the mood score, theamplifier value, and the damper value for each text segment is largerthan 0.5, then such sum is assigned to the text segment as the adjustedmood score. If the negation value of the text segment is not 1 orlarger, and if a sum of the mood score, the amplifier value and thedamper value for each text segment is no larger than 0.5, the adjustedmood score for the text segment is set to 0.5. If the negation value ofthe text segment is 1 or larger, then the mood associated with the textsegment is adjusted to an opposite mood and the adjusted mood score forthe text segment is set to 1. Table 4 shows an example of the newdataset.

TABLE 4 Adjusted Text Mood Mood Adjusted Segment Mood Score AmplifierDamper Negation Score Mood content composed 2 2 −1 . 3 composed capableconfident 3 . . 1 1 unsure

Once the adjusted mood scores have been determined for each textsegment, overall mood scores may be generated for each of the documents406 by summing the adjusted mood scores of the text segments indicativeof moods in the document. A resulting dataset includes an ID for eachdocument and overall mood scores of different moods (e.g., one or moreof the twelve moods as shown in FIG. 2) for each document. Table 5 showsan example of such a dataset.

TABLE 5 DOC ID CONFUSED ELATED TIRED UNSURE HOSTILE CONFIDENT AGREEABLE30 2 . 1 . . 6 .

A final dataset may be generated to include overall mood scores ofdifferent mood scales (e.g., one or more of the six mood scales as shownin FIG, 2) for each document by subtracting the overall mood score of anegative mood from the overall mood score of the corresponding positivemood in a same mood scale. The final dataset may be joined back with thedocuments 406 to obtain information such as original texts and dates ofthe documents 406. For example, the mood state datasets 402 may includethe final dataset joined with the documents 40 the final dataset, and/orany other datasets discussed above.

The mood state measurements 404 can be implemented in various contexts.For example, a company engaging in a marketing campaign for a particularproduct can set up a clearing house including a mood measurements systemwhich automatically extracts documents related to the product fromcertain social media websites and generates mood state datasets for theextracted documents. The generated mood state datasets, together withconsumer information, can provide detailed information indicating howdifferent groups of consumers feel about the product so that marketingefforts can be more focused to improve efficiency. As another example,mood state measurements may be used for data mining in various businessapplications, such as customer relationship management. In addition, themood state measurements can be applied to data in various analyticcapacities, such as data enrichment for econometric models, augmentationof exogenous variable space for greater prediction accuracy,identification of leading and lagging indicators, prediction of one-timeevents (e.g., new product launches, movie premiers, breaking news), andanalytic survey data enrichment (e.g., employee satisfaction, netpromoter).

FIG. 5 depicts an example flow diagram for determining an overall moodscore of a document. At 502, a text segment indicating a mood isidentified in the document. At 504, the identified text segment ismapped to a mood scale, and at 506 a mood weight is generated for thetext segment. The mood scale may be one of a predetermined set of moodscales. At 508, an overall mood score is determined for the documentbased at least in pa on the mood weight of the text segment.

FIG. 6 depicts a computer-implemented environment wherein users 602 caninteract with a mood state measurement system 610 hosted on one or moreservers 606 through one or more networks 604. One or more servers 606accessible through the network(s) 604 can host the mood statemeasurement system 610. The one or more servers 606 can also contain orhave access to one or more data stores 608 for storing data for the moodstate measurement system 610.

This written description uses examples to disclose the invention,including the best mode, and also to enable a person skilled in the artto make and use the invention. The patentable scope of the invention mayinclude other examples. For example, a computer-implemented system andmethod can be configured such that a mood state measurement system canbe provided on a stand-alone computer for access by a user, such asshown at 700 in FIG. 7.

Additionally, the methods and systems described herein may beimplemented on many different types of processing devices by programcode comprising program instructions that are executable by the deviceprocessing subsystem. The software program instructions may includesource code, object code, machine code, or any other stored data that isoperable to cause a processing system to perform the methods andoperations described herein. Other implementations may also be used,however, such as firmware or even appropriately designed hardwareconfigured to carry out the methods and systems described herein.

The systems' and methods' data (e.g., associations, mappings, datainput, data output, intermediate data results, final data results, etc.)may be stored and implemented in one or more different types ofcomputer-implemented data stores, such as different types of storagedevices and programming constructs (e.g., RAM, ROM, Flash memory, flatfiles, databases, programming data structures, programming variables,IF-THEN (or similar type) statement constructs, etc.). it is noted thatdata structures describe formats for use in organizing and storing datain databases, programs, memory, or other computer-readable media for useby a computer program.

The systems and methods may be provided on many different types ofcomputer readable media including computer storage mechanisms (e.g.,CD-ROM, diskette, RAM, flash memory, computer's hard drive, etc.) thatcontain instructions (e.g., software) for use in execution by aprocessor to perform the methods' operations and implement the systemsdescribed herein.

The computer components, software modules, functions, data stores anddata structures described herein may be connected directly or indirectlyto each other in order to allow the flow of data needed for theiroperations. it is also noted that a module or processor includes but isnot limited to a unit of code that performs a software operation, andcan be implemented for example as a subroutine unit of code, or as asoftware function unit of code, or as an object (as in anobject-oriented paradigm), or as an applet, or in a computer scriptlanguage, or as another type of computer code. The software componentsand/or functionality may be located on a single computer or distributedacross multiple computers depending upon the situation at hand.

It should be understood that as used in the description herein andthroughout the claims that follow, the meaning of “a,” “an,” and “the”includes plural reference unless the context clearly dictates otherwise.Also, as used in the description herein and throughout the claims thatfollow, the meaning of “in” includes “in” and “on” unless the contextclearly dictates otherwise. Finally, as used in the description hereinand throughout the claims that follow, the meanings of “and” and “or”include both the conjunctive and disjunctive and may be usedinterchangeably unless the context expressly dictates otherwise; thephrase “exclusive or” may be used to indicate situation where only thedisjunctive meaning may apply.

The invention claimed is:
 1. A computer-implemented method comprising:accessing, using one or more processors, a mood evaluation index from afirst electronic data store, wherein the mood evaluation index maps textto mood categories, wherein text describes a state associated with atleast one of the mood categories, and wherein a mood category isassociated with a state; mapping text to a mood category using the moodevaluation index, wherein the mood evaluation index associates a statewith the text; calculating a mood weight for the text, wherein moodweights are calculated using a mood evaluation index, and wherein moodweights represent an intensity of the state that the text describes;accessing a message from a second electronic data store, wherein themessage includes text mapped by the mood evaluation index; using themood evaluation index to identify text included in the message that ismapped to a mood category; determining whether a modifier is inproximity to identified text; adjusting the mood weight of theidentified text that is in proximity to the modifier; summing the moodweights of the identified text, wherein summing includes determining acategory score corresponding to the mood category; and outputting thecategory score corresponding to the mood category to a third electronicdata store.
 2. The computer-implemented method of claim 1, wherein themodifier is an amplifier, a damper or a negation.
 3. The method of claim1, wherein the modifier is an amplifier, and wherein the adjusting themood weight of the identified text that is in proximity to the modifierincludes increasing the mood weight.
 4. The computer-implemented methodof claim 1, wherein the modifier is a damper, and wherein the adjustingthe mood weight of the identified text that is in proximity to themodifier includes decreasing the mood weight.
 5. Thecomputer-implemented method of claim 1, wherein the mood weight isalphanumeric.
 6. The computer-implemented method of claim 1, whereinsumming the mood weights accounts for repeated use, within the message,of the same text mapped to the mood category.
 7. Thecomputer-implemented method of claim 1, wherein multiple words aremapped to a same mood category within the mood evaluation index.
 8. Thecomputer-implemented method of claim 1, wherein the identified text ismapped to more than one mood category.
 9. The computer-implementedmethod of claim 1, wherein the modifier is a negation, wherein theadjusting the mood weight of the identified text that is in proximity tothe modifier includes changing a positive mood to a negative mood orchanging a negative mood to a positive mood.
 10. Thecomputer-implemented method of claim 9, wherein the adjusting the moodweight of the identified text that is in proximity to the modifierfurther includes decreasing the mood weight.
 11. Thecomputer-implemented method of claim 1, further comprising: accessingone or more mood scales, wherein the mood scales comprise at least onepositive mood and a corresponding negative mood.
 12. Thecomputer-implemented method of claim 1, wherein the message is accessedfrom social media or social networking.
 13. The computer-implementedmethod of claim 1, wherein the message is accessed from an informationdatabase.
 14. The computer-implemented method of claim 1, furthercomprising generating a dataset by joining a dataset of the moodcategory with a dataset for the modifier, wherein the dataset for themodifier includes a dataset for at least one of an amplifier category, adamper category and a negation category.
 15. The computer-implementedmethod of claim 1, further comprising generating a dataset that includesoverall mood scores of different mood scales by subtracting an overallmood score of a negative mood from an overall mood score of acorresponding positive mood in a same mood scale.
 16. A computer-programproduct tangibly embodied in a non-transitory, computer-readable storagemedium that stores instructions, the instructions executable by a dataprocessing apparatus for performing operations including: accessing,using one or more processors, a mood evaluation index from a firstelectronic data store, wherein the mood evaluation index maps text tomood categories, wherein text describes a state associated with at leastone of the mood categories, and wherein a mood category is associatedwith a state; mapping text to a mood category using the mood evaluationindex, wherein the mood evaluation index associates a state with thetext; calculating a mood weight for the text, wherein mood weights arecalculated using a mood evaluation index, and wherein mood weightsrepresent an intensity of the state that the text describes; accessing amessage from a second electronic data store, wherein the messageincludes text mapped by the mood evaluation index; using the moodevaluation index to identify text included in the message that is mappedto a mood category; determining whether a modifier is in proximity toidentified text; adjusting the mood weight of the identified text thatis in proximity to the modifier; summing the mood weights of theidentified text, wherein summing includes determining a category scorecorresponding to the mood category; and outputting the category scorecorresponding to the mood category to a third electronic data store,wherein the modifier is a negation, and wherein the adjusting the moodweight of the identified text that is in proximity to the modifierincludes changing one of a positive mood to a negative mood or changinga negative mood to a positive mood.
 17. The computer-program product ofclaim 16, wherein the modifier is a negation, wherein the instructionsexecutable by the data processing apparatus for performing operationsfor adjusting the mood weight of the identified text that is inproximity to the modifier includes changing a positive mood to anegative mood or changing a negative mood to a positive mood.
 18. Thecomputer-program product of claim 17, wherein the instructionsexecutable by the data processing apparatus for performing operationsfor adjusting the mood weight of the identified text that is inproximity to the modifier further includes decreasing the mood weight.19. The computer-program product of claim 16, further comprisinginstructions executable by the data processing apparatus for performingoperations for accessing one or more mood scales, wherein the moodscales comprise at least one positive mood and a corresponding negativemood.
 20. The computer-program product of claim 16, wherein the messageis accessed from social media or social networking.
 21. Thecomputer-program product of claim 16, wherein the message is accessedfrom an information database.
 22. The computer-program product of claim16, further comprising instructions executable by the data processingapparatus for performing operations for generating a dataset by joininga dataset of the mood category with a dataset for the modifier, whereinthe dataset for the modifier includes a dataset for at least one of anamplifier category, a damper category and a negation category.
 23. Thecomputer-program product of claim 16, further comprising instructionsexecutable by the data processing apparatus for performing operationsfor generating a dataset that includes overall mood scores of differentmood scales by subtracting an overall mood score of a negative mood froman overall mood score of a corresponding positive mood in a same moodscale.
 24. A system comprising: one or more processors configured toperform operations that include: accessing, using the one or moreprocessors, a mood evaluation index from a first electronic data store,wherein the mood evaluation index maps text to mood categories, whereintext describes a state associated with at least one of the moodcategories, and wherein a mood category is associated with a state;mapping text to a mood category using the mood evaluation index, whereinthe mood evaluation index associates a state with the text; calculatinga mood weight for the text, wherein mood weights are calculated using amood evaluation index, and wherein mood weights represent an intensityof the state that the text describes; accessing a message from a secondelectronic data store, wherein the message includes text mapped by themood evaluation index; using the mood evaluation index to identify textincluded in the message that is mapped to a mood category; determiningwhether a modifier is in proximity to identified text; adjusting themood weight of the identified text that is in proximity to the modifier;summing the mood weights of the identified text, wherein summingincludes determining a category score corresponding to the moodcategory; and outputting the category score corresponding to the moodcategory to a third electronic data store, wherein the modifier is anegation, and wherein the adjusting the mood weight of the identifiedtext that is in proximity to the modifier includes changing one of apositive mood to a negative mood or changing a negative mood to apositive mood.
 25. The system of claim 24, wherein the modifier is anegation, wherein the operations for adjusting the mood weight of theidentified text that is in proximity to the modifier includes changing apositive mood to a negative mood or changing a negative mood to apositive mood.
 26. The system of claim 25, wherein the operations foradjusting the mood weight of the identified text that is in proximity tothe modifier further includes decreasing the mood weight.
 27. The systemof claim 24, further comprising operations for accessing one or moremood scales, wherein the mood scales comprise at least one positive moodand a corresponding negative mood.
 28. The system of claim 24, whereinthe message is accessed from social media or social networking.
 29. Thesystem of claim 24, wherein the message is accessed from an informationdatabase.
 30. The system of claim 24, further comprising operations forgenerating a dataset by joining a dataset of the mood category with adataset for the modifier, wherein the dataset for the modifier includesa dataset for at least one of an amplifier category, a damper categoryand a negation category.
 31. The system of claim 24, further comprisingoperations for generating a dataset that includes overall mood scores ofdifferent mood scales by subtracting an overall mood score of a negativemood from an overall mood score of a corresponding positive mood in asame mood scale.